Decadal variation of predictive skill of seasonal climate over the Yangtze River and its possible causes

Seasonal climate predictions with global climate models which are developed based on the ocean-atmosphere interactions, contribute to the water resources management and hazard mitigation. Nowadays, multi-model ensemble seasonal climate prediction system, such as North American Multi-Model Ensemble (NMME), has become an effective way to provide useful forecast information a few months ahead especially over regions with strong ocean-atmosphere coupling. Previous studies have evaluated the skill of NMME hindcasts worldwide, however, it’s still unclear that whether the NMME real-time forecasts perform as well as the hindcasts and how the changes in ocean-atmospheric teleconnections affect the prediction skill. Here we show that although selecting an appropriate time frame for the calculation of climatology can reduce errors of real-time prediction, the real-time prediction skills are lower than hindcast skills in the Yangtze River basin, with anomaly correlation decreased by 14%-51% (38%-75%) and error increased by 30%-31% (51%-55%) for seasonal precipitation (temperature) predictions up to the sixth lead-seasons, and the skill decrease larger at longer leads. The failure in representing the decadal variations of ocean-atmospheric teleconnection (especially the association with Indian Ocean surface temperature) during the real-time forecast period can partly explain the decline in the prediction skills. Our findings suggest that improved simulations of the changes in the ocean-atmospheric teleconnections are necessary for skillful seasonal climate predictions in the real-time.